{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 线性SVM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.1   -0.098 -0.096 ...  1.096  1.098  1.1  ]\n",
      " [-0.1   -0.098 -0.096 ...  1.096  1.098  1.1  ]\n",
      " [-0.1   -0.098 -0.096 ...  1.096  1.098  1.1  ]\n",
      " ...\n",
      " [-0.1   -0.098 -0.096 ...  1.096  1.098  1.1  ]\n",
      " [-0.1   -0.098 -0.096 ...  1.096  1.098  1.1  ]\n",
      " [-0.1   -0.098 -0.096 ...  1.096  1.098  1.1  ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "\n",
    "data = np.array([\n",
    "    [0.1, 0.7],\n",
    "    [0.3, 0.6],\n",
    "    [0.4, 0.1],\n",
    "    [0.5, 0.4],\n",
    "    [0.8, 0.04],\n",
    "    [0.42, 0.6],\n",
    "    [0.9, 0.4],\n",
    "    [0.6, 0.5],\n",
    "    [0.7, 0.2],\n",
    "    [0.7, 0.67],\n",
    "    [0.27, 0.8],\n",
    "    [0.5, 0.72]\n",
    "])# 建立数据集\n",
    "label = [1] * 6 + [0] * 6 #前六个数据的label为1后六个为0\n",
    "x_min, x_max = data[:, 0].min() - 0.2, data[:, 0].max() + 0.2\n",
    "y_min, y_max = data[:, 1].min() - 0.2, data[:, 1].max() + 0.2\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.002),\n",
    "                     np.arange(y_min, y_max, 0.002)) # meshgrid如何生成网格\n",
    "print(xx)\n",
    "model_linear = svm.SVC(kernel='linear', C = 0.001)# 线性svm\n",
    "model_linear.fit(data, label) # 训练\n",
    "Z = model_linear.predict(np.c_[xx.ravel(), yy.ravel()]) # 预测\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.contourf(xx, yy, Z, cmap = plt.cm.ocean, alpha=0.6)\n",
    "plt.scatter(data[:6, 0], data[:6, 1], marker='o', color='r', s=100, lw=3) \n",
    "plt.scatter(data[6:, 0], data[6:, 1], marker='x', color='k', s=100, lw=3)\n",
    "plt.title('Linear SVM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多项式SVM\n",
    "对比不同最高次数的分类情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 15))\n",
    " \n",
    "for i, degree in enumerate([1, 3, 5, 7, 9, 12]):#多项式次数选择了1,3,5,7,9,12\n",
    "    # C: 惩罚系数，gamma: 高斯核的系数\n",
    "    model_poly = svm.SVC(C=0.0001, kernel='poly', degree=degree) # 多项式核\n",
    "    model_poly.fit(data, label)# 训练\n",
    "    # ravel - flatten\n",
    "    # c_ - vstack\n",
    "    # 把后面两个压扁之后变成了x1和x2，然后进行判断，得到结果在压缩成一个矩形\n",
    "    Z = model_poly.predict(np.c_[xx.ravel(), yy.ravel()])#预测\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    plt.subplot(3, 2, i + 1)\n",
    "    plt.subplots_adjust(wspace=0.4, hspace=0.4)\n",
    "    plt.contourf(xx, yy, Z, cmap=plt.cm.ocean, alpha=0.6)\n",
    " \n",
    "    # 画出训练点\n",
    "    plt.scatter(data[:6, 0], data[:6, 1], marker='o', color='r', s=100, lw=3)\n",
    "    plt.scatter(data[6:, 0], data[6:, 1], marker='x', color='k', s=100, lw=3)\n",
    "    plt.title('Poly SVM with $\\degree=$' + str(degree))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 高斯核SVM\n",
    "\n",
    "对比不同gamma下的分类情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 15))\n",
    " \n",
    "for i, gamma in enumerate([1, 5, 15, 35, 45, 55]):\n",
    "    # C: 惩罚系数，gamma: 高斯核的系数\n",
    "    model_rbf = svm.SVC(kernel='rbf', gamma=gamma, C= 0.0001).fit(data, label)\n",
    " \n",
    "    # ravel - flatten\n",
    "    # c_ - vstack\n",
    "    # 把后面两个压扁之后变成了x1和x2，然后进行判断，得到结果在压缩成一个矩形\n",
    "    Z = model_rbf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    plt.subplot(3, 2, i + 1)\n",
    "    plt.subplots_adjust(wspace=0.4, hspace=0.4)\n",
    "    plt.contourf(xx, yy, Z, cmap=plt.cm.ocean, alpha=0.6)\n",
    " \n",
    "    # 画出训练点\n",
    "    plt.scatter(data[:6, 0], data[:6, 1], marker='o', color='r', s=100, lw=3)\n",
    "    plt.scatter(data[6:, 0], data[6:, 1], marker='x', color='k', s=100, lw=3)\n",
    "    plt.title('RBF SVM with $\\gamma=$' + str(gamma))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试不同SVM在Mnist数据集上的分类情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(60000,)\n",
      "Train...\n",
      "Train：0min2.216sec\n",
      "accuracy:  0.95\n",
      "测试集准确率： 0.95\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "import numpy as np\n",
    "from time import time\n",
    "from sklearn.metrics import accuracy_score\n",
    "from struct import unpack\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "def readimage(path):\n",
    "    with open(path, 'rb') as f:\n",
    "        magic, num, rows, cols = unpack('>4I', f.read(16))\n",
    "        img = np.fromfile(f, dtype=np.uint8).reshape(num, 784)\n",
    "    return img\n",
    "\n",
    "def readlabel(path):\n",
    "    with open(path, 'rb') as f:\n",
    "        magic, num = unpack('>2I', f.read(8))\n",
    "        lab = np.fromfile(f, dtype=np.uint8)\n",
    "    return lab\n",
    "\n",
    "train_data  = readimage(\"datasets/MNIST/raw/train-images-idx3-ubyte\")#读取数据\n",
    "train_label = readlabel(\"datasets/MNIST/raw/train-labels-idx1-ubyte\")\n",
    "test_data   = readimage(\"datasets/MNIST/raw/t10k-images-idx3-ubyte\")\n",
    "test_label  = readlabel(\"datasets/MNIST/raw/t10k-labels-idx1-ubyte\")\n",
    "print(train_data.shape)\n",
    "print(train_label.shape)\n",
    "#数据集中数据太多，为了节约时间，我们只使用前2000张进行训练\n",
    "train_data=train_data[:2000]\n",
    "train_label=train_label[:2000]\n",
    "test_data=test_data[:200]\n",
    "test_label=test_label[:200]\n",
    "svc=svm.SVC()\n",
    "parameters = {'kernel':['rbf'], 'C':[1]}#使用了高斯核\n",
    "print(\"Train...\")\n",
    "clf=GridSearchCV(svc,parameters,n_jobs=-1)\n",
    "start = time()\n",
    "clf.fit(train_data, train_label)\n",
    "end = time()\n",
    "t = end - start\n",
    "print('Train：%dmin%.3fsec' % (t//60, t - 60 * (t//60)))#显示训练时间\n",
    "prediction = clf.predict(test_data)#对测试数据进行预测\n",
    "print(\"accuracy: \", accuracy_score(prediction, test_label))\n",
    "accurate=[0]*10\n",
    "sumall=[0]*10\n",
    "i=0\n",
    "j=0\n",
    "while i<len(test_label):#计算测试集的准确率\n",
    "    sumall[test_label[i]]+=1\n",
    "    if prediction[i]==test_label[i]:\n",
    "        j+=1\n",
    "    i+=1\n",
    "print(\"测试集准确率：\",j/200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train...\n",
      "Train：0min2.158sec\n",
      "accuracy:  0.935\n",
      "测试集准确率： 0.935\n"
     ]
    }
   ],
   "source": [
    "parameters = {'kernel':['poly'], 'C':[1]}#使用了多项式核\n",
    "print(\"Train...\")\n",
    "clf=GridSearchCV(svc,parameters,n_jobs=-1)\n",
    "start = time()\n",
    "clf.fit(train_data, train_label)\n",
    "end = time()\n",
    "t = end - start\n",
    "print('Train：%dmin%.3fsec' % (t//60, t - 60 * (t//60)))\n",
    "prediction = clf.predict(test_data)\n",
    "print(\"accuracy: \", accuracy_score(prediction, test_label))\n",
    "accurate=[0]*10\n",
    "sumall=[0]*10\n",
    "i=0\n",
    "j=0\n",
    "while i<len(test_label):#计算测试集的准确率\n",
    "    sumall[test_label[i]]+=1\n",
    "    if prediction[i]==test_label[i]:\n",
    "        j+=1\n",
    "    i+=1\n",
    "print(\"测试集准确率：\",j/200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train...\n",
      "Train：0min1.431sec\n",
      "accuracy:  0.955\n",
      "测试集准确率： 0.955\n"
     ]
    }
   ],
   "source": [
    "parameters = {'kernel':['linear'], 'C':[1]}#使用了线性核\n",
    "print(\"Train...\")\n",
    "clf=GridSearchCV(svc,parameters,n_jobs=-1)\n",
    "start = time()\n",
    "clf.fit(train_data, train_label)\n",
    "end = time()\n",
    "t = end - start\n",
    "print('Train：%dmin%.3fsec' % (t//60, t - 60 * (t//60)))\n",
    "prediction = clf.predict(test_data)\n",
    "print(\"accuracy: \", accuracy_score(prediction, test_label))\n",
    "accurate=[0]*10\n",
    "sumall=[0]*10\n",
    "i=0\n",
    "j=0\n",
    "while i<len(test_label):#计算测试集的准确率\n",
    "    sumall[test_label[i]]+=1\n",
    "    if prediction[i]==test_label[i]:\n",
    "        j+=1\n",
    "    i+=1\n",
    "print(\"测试集准确率：\",j/200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "42c9bd6a7bb2602a627a03adc5d7029ff8a60f7e68b58a1beb81d0b4574fea3d"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
